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Bilateral filter-based adaptive nonuniformity correction for infrared focal-plane array systems

机译：基于双边滤波器的红外焦平面阵列系统自适应非均匀性校正

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In scene-based nonuniformity correction (NUC) methods for infrared focal-plane array cameras, the problem of ghosting artifacts widely affects the sensitivity of the imaging system and visibly decreases the image quality. Ghosting artifacts can also degrade the performance of several applications, such as target detection and tracking. We carried out a detailed analysis of the problem using a well-established NUC technique: the least mean square Scribner's algorithm. In order to solve some drawbacks of the original Scribner's algorithm, we introduced in the scheme a new technique that mitigates ghosting. Such technique relies on the employment of an edge-preserving spatial filter for the purpose of computing reliable spatial estimates. We tested the effectiveness of the new technique applying the improved NUC method to an experimental IR sequence of frames acquired in the laboratory. Finally, the performance of the proposed method was discussed and compared to that yielded by a well-established deghosting technique.
机译：在用于红外焦平面阵列相机的基于场景的非均匀性校正（NUC）方法中，重影伪影的问题广泛影响成像系统的灵敏度，并明显降低图像质量。重影工件还会降低某些应用程序的性能，例如目标检测和跟踪。我们使用一种完善的NUC技术对问题进行了详细分析：最小均方Scribner算法。为了解决原始Scribner算法的一些缺点，我们在方案中引入了一种减轻重影的新技术。为了计算可靠的空间估计，这种技术依赖于使用保留边缘的空间滤波器。我们测试了将改进的NUC方法应用于实验室中获得的帧的实验IR序列的新技术的有效性。最后，讨论了所提出方法的性能，并将其与成熟的反虚幻技术产生的性能进行了比较。

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